Root canal segmentation from cone-beam computed tomography guided by micro-computed tomography based on deep learning

Root canal segmentation from cone-beam computed tomography guided by micro-computed tomography based on deep learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Root canal segmentation from cone-beam computed tomography guided by micro-computed tomography based on deep learning Xianhua Gao, Jingzhi Ma, Bo Li, Yimeng Fang, Lianting Hu, Min Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7053178/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in BMC Oral Health → Version 1 posted 14 You are reading this latest preprint version Abstract Background Accurate root canal segmentation from cone-beam computed tomography (CBCT) is essential for endodontic diagnosis and treatment planning. This study aims to explore the feasibility of using deep learning (DL) models, trained on CBCT images of extracted teeth guided by micro-computed tomography (µCT), for clinical CBCT image segmentation. Methods A dataset of 56 extracted teeth with diverse root canal complexities was constructed, combining CBCT and µCT scans. Ground truth annotations were derived from µCT-based masks and registered to CBCT images. DL models based on U 2 -Net architecture were trained and evaluated for tooth and root canal segmentation, comparing µCT-guided and manual-label-based approaches. The effects of field-of-view (FOV) size and interpolation algorithms on segmentation performance were investigated. The trained models were applied to clinical CBCT images, achieving rapid and accurate root canal segmentation validated by endodontic specialists. Results The µCT-guided AI segmentation method outperformed the manual-label-based approach. Combining a limited FOV with an interpolation algorithm demonstrated notable advantages in capturing intricate details. In segmenting root canal from patient CBCT images, 94% of single rooted teeth and 100% of molars, were rated as “excellent” or “good”. Conclusions Results demonstrated the potential of µCT-guided DL models for enhancing root canal segmentation in clinical practice, offering a promising tool for digital dentistry. Clinical trial number: not applicable Deep learning AI segmentation mode root canal segmentation cone-beam computed tomography micro-computed tomography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background A thorough understanding of root canal anatomy is fundamental to the success of endodontic treatments [ 1 ]. Micro-computed tomography (µCT) is widely regarded as the gold standard for imaging and analyzing root canal systems due to its ability to provide high-resolution, high-contrast images [ 2 ]. These images enable the use of simple thresholding techniques to automatically segment root canals, thereby facilitating accurate three-dimensional (3D) reconstructions of internal tooth structures [ 3 , 4 ]. However, the clinical application of µCT is limited by its long acquisition times, high radiation doses, and narrow field of view (FOV), restricting its use primarily to imaging small live animals or specific organs in humans and large animals. Cone-beam computed tomography (CBCT) has emerged as a practical alternative to µCT in dental imaging, offering multi-planar visualization with a wide FOV that can range from a few teeth to full craniofacial coverage. [ 5 , 6 ]. Its advantages include shorter scanning times and lower radiation doses, making it suitable for clinical 3D dental radiology. [ 7 , 8 ]. Despite these benefits, CBCT images often suffer from blurring and are presented as two-dimensional sequences, requiring clinicians to rely heavily on their experience to interpret the internal 3D structure of teeth. Consequently, there is a pressing need to advance automated and precise root canal segmentation (RCS) techniques from CBCT images, particularly for applications in digital dentistry such as guided endodontics [ 9 – 11 ]. The segmentation of CBCT images presents several challenges, including noise, limited resolution, beam hardening artifacts, and the inherent morphological variability of dental anatomy [ 12 ]. Traditional thresholding methods, which are effective for µCT segmentation, are less reliable for CBCT data [ 2 ]. The Level Set method, which tracks contour changes across consecutive slices, is prone to over-segmentation [ 13 ]. Graph cut algorithms require precise location information and user interaction [ 14 , 15 ], while template-based fitting methods struggle with the segmentation of multi-rooted teeth[ 16 ]. Deep Learning (DL), a subset of artificial intelligence (AI), has recently gained significant attention for its potential in medical imaging [ 17 – 19 ]. By leveraging Convolutional Neural Networks (CNNs), DL can integrate both low- and high-level features, outperforming traditional methods in accuracy and speed [ 20 – 23 ]. Despite these advancements, research focusing on RCS from CBCT images using DL remains limited [ 24 , 25 ]. Studies have explored several DL models, such as U-Net, residual U-Net, Xception U-Net, and enhanced 3D deep neural networks, for RCS from CBCT images with varying voxel sizes [ 26 – 30 ]. However, accurately detecting the intricate and variable features of root canals, particularly in the apical region, remains a significant challenge. Wang et al. improved this issue by optimizing the network architecture and introducing a new evaluation metric to measure distance errors near the apical foramen. However, their study had certain limitations. Firstly, the research scope was confined to anterior teeth and premolars. Secondly, the study relied on manual annotations as ground truth [ 31 ]. It is noteworthy that manual annotation is not only time-consuming but also prone to significant intra- and inter-observer variability. Additionally, due to the inherently low accuracy of visually identifying fine and irregular root canals, this method has intrinsic limitations in terms of precision. Given that supervised DL models depend on the anatomical correspondence between the input image and the ground truth, obtaining accurate ground truth data is critical for achieving optimal results. High-precision segmentation masks can be automatically generated from µCT data using simple thresholding methods. Lin et al. adopted an innovative approach by applying a micro-CT-guided segmentation model to segment clinical CBCT images of patients. This method automatically generates high-precision segmentation masks from µCT data using a simple thresholding technique and establishes a mapping relationship with patient CBCT images through image registration. Nevertheless, the segmentation of fine root canals remains challenging. This might stem from the constraints of the training data, which exclusively included premolars extracted for orthodontic purposes, resulting in a lack of diversity in tooth types and morphological complexity.[ 30 ]. To enhance the representativeness of the data, it is essential to include teeth from various positions and root canal systems with differing complexities. However, acquiring such a dataset—comprising pre-teeth-extraction patient CBCT images and post-teeth-extraction micro-CT images—poses significant practical challenges. The Tooth and Root Canal Morphology Database (School of Stomatology, Wuhan University, China) comprises over 3,000 extracted teeth and their corresponding µCT images will serve as a significant data support for automated segmentation of dental and root canal systems. Therefore, this study aims to explore the feasibility of applying DL segmentation models, trained on CBCT images of extracted teeth for clinical patient CBCT images segmentation. In brief, firstly, we trained, evaluated and compared the AI segmentation models for segmenting tooth and root canal from CBCT images of isolated teeth, both with and without the guidance of µCT (µCT-guided AI segmentation vs. manual-label-based AI segmentation). The effects of FOV size and interpolation algorithm on RCS performance were studied. Subsequently, the established mapping relationship between CBCT and µCT of isolated teeth was utilized for RSC from patient CBCT images. This approach enabled rapid and accurate RSC, which was validated by endodontic specialists, demonstrating its potential feasibility for clinical application. Methods Samples and data collection of isolated teeth Teeth samples: A total of 56 extracted teeth, comprising 28 single-rooted teeth (SR) and 28 molars (M), were selected from the Tooth and Root Canal Morphology Database. All selected teeth exhibited fully formed roots and were free from cracks, restorations, root fillings, or root resorptions. µCT imaging: The teeth were initially scanned using a µCT system (µCT-50; Scanco Medical, Bassersdorf, Switzerland) with the following parameters: isotropic resolution of 30 µm, scanning vial diameter of 48 mm, voltage of 90 kVp, current of 88 mA, power of 8 W, integration time of 500 ms, and 500 projections per 180°. Scanning was performed perpendicular to the longitudinal axis of the root. CBCT imaging: Subsequently, the teeth were scanned using both large- and limited- FOV CBCT systems. Large-FOV CBCT scans were performed using the NewTom 5G system (Quantitative Radiology, Verona, Italy) with a FOV of 15 cm × 12 cm, isotropic resolution of 200 µm, voltage of 110 kVp, current of 3.0 mA, and exposure time of 3.6 s. Limited-FOV CBCT scans were conducted using the 3D Accuitomo 170 system (J Morita Mfg. Corp., Kyoto, Japan) with a FOV of 4 cm × 4 cm, isotropic resolution of 80 µm, voltage of 90 kV, and current of 5 mA. Training samples acquisition To address the significant variation in tooth size and ensure consistent image dimensions for training, the datasets for single-rooted teeth (SR) and molars (M) were preprocessed. Large- and limited-FOV CBCT volumes were cropped to dimensions of 64 × 64 and 160 × 160 for SR, and 80 × 80 and 200 × 200 for M, respectively. The depth of the cropped volumes was adjusted according to the tooth length, ensuring complete coverage of the tooth while retaining sufficient background context for segmentation. In the control group (manual-label-based AI segmentation), two endodontists manually annotated the tooth and root canal structures. An experienced endodontist reviewed and refined these annotations using MITK software. In the experimental group (µCT-guided AI segmentation), 3D tooth models with root canal systems were generated from µCT data using a thresholding method in Mimics software (v18.0; Materialise, Leuven, Belgium). These tooth masks, containing root canals, were imported into the Medical Imaging Interaction Toolkit Workbench (MITK, version 2022.04; available at http://mitk.org ) and registered to CBCT images using the rigidICP.3D.default algorithm. Following registration, µCT masks were normalized to match the resolution of the CBCT images. Training samples were created by mapping the tooth and root canal masks onto the CBCT images. For both groups, the registered 3D models derived from µCT data served as the ground truth for the test set. The SR and M samples were divided into a training set (18 teeth), a validation set (5 teeth), and a test set (5 teeth). All data were exported in NIFTI format. To evaluate the impact of interpolation on RCS using the µCT-guided AI model, cropped images with a voxel size of 200 µm were refined to 100 µm, 80 µm, 50 µm, and 40 µm. Similarly, images with an initial voxel size of 80 µm were interpolated to 50 µm and 40 µm using linear interpolation. The 3D visualization of RCS was compared and analyzed across different FOVs and interpolation levels. Training and testing of AI segmentation models The U 2 -Net architecture, a robust deep CNN designed for salient object segmentation, was employed as the AI segmentation model in this study[ 32 ]. The model was iteratively trained to optimize performance, with the framework comprising two stages: TS and RCS, as illustrated in Fig. 1 A. For the training and validation sets, RCS images were extracted within tooth mask boundaries using MITK software. In the test set, teeth were segmented based on predicted tooth contours. The DL algorithms and models were implemented using PyCharm Community Edition (Version 2022.2.3 x64) and Python (Version 3.9) on a Windows 10 system equipped with NVIDIA RTX A5000 GPU (24GB memory). The PyTorch framework (version 1.7.0; available at https://pytorch.org ) was utilized for model implementation, with optimization performed using the Adam optimizer at an initial learning rate of 10⁻³. Training was conducted for 400 epochs per group, with durations ranging from 1.3 to 50 hours depending on dataset size. The full-sized U 2 -Net architecture was applied applied, and data augmentation techniques, including random rotation and clipping, were employed to mitigate overfitting. Quantitative and qualitative evaluation The following metrics were used to evaluate the voxel-matching accuracy of the segmentation models: Dice Similarity Coefficient (DSC): $$\:\begin{array}{c}DSC\:=\:\frac{2\left|{V}_{gt}\cap\:\:{V}_{seg}\right|}{\left|{V}_{gt}\right|+\left|{V}_{seg}\right|}\#\left(1\right)\end{array}$$ Sensitivity (SEN/Recall): $$\:\begin{array}{c}SEN\:=\frac{\left|{V}_{gt}\cap\:{V}_{seg}\right|}{\left|{V}_{gt}\:\right|}\#\left(2\right)\end{array}$$ Intersection over Union (IOU): $$\:\begin{array}{c}IOU=\frac{\left|{V}_{gt}\cap\:{V}_{seg}\right|}{\left|{V}_{gt}\cup\:{V}_{seg}\right|}\#\left(3\right)\end{array}$$ Where \(\:{V}_{gt}\) and \(\:{V}_{seg}\) represent the voxel sets of the ground truth (label data) and the model segmentation, respectively. Higher values of DSC, SEN, and IOU indicate superior performance. Visual comparisons between manual-label-based and µCT-guided AI segmentation models were conducted using MITK and GOM Inspect Pro software (GOM Software 2022, GOM GmbH, Braunschweig, Germany). Surface deviation analysis was performed for TS (-0.5 mm to + 0.5 mm) and RCS (-0.2 mm to + 0.2 mm). RCS from patient CBCT images In the clinical application of the established mapping relationship between isolated teeth CBCT and µCT, limited FOV CBCT image series were collected from 20 anonymous patients. Scans were acquired using a 3D Accuitomo 170 system (J Morita Mfg. Corp., Kyoto, Japan) with a FOV of 4 cm × 4 cm, operating at 90 kV and 5 mA, and a voxel size of 80 µm³. A total of 29 teeth (17 SR and 12 M) without restorations or fractures were extracted as bounding boxes. Axial image sizes were standardized to 160 × 160 for SR and 200 × 200 for M. Teeth were manually labeled from patient CBCT scans, and regions of interest (ROIs) within tooth contours were extracted for RCS. As depicted in Fig. 1 B, the extracted tooth images were input into the pre-trained µCT-guided AI segmentation model for automatic RSC. Results were qualitatively assessed by three endodontists and categorized as “excellent”, “good”, “fair”, or “poor”. Disagreements were resolved through consensus discussions. Statistical analysis The normality of the data was assessed using the Shapiro-Wilk test. For paired data following a normal distribution, the paired t-test was applied, whereas the Wilcoxon signed-rank test was used for nonparametric data. A significance level of p < 0.05 was adopted for all analyses. All statistical evaluations were performed using SPSS 16.0 software. Results Performance of the µCT-Guided AI Segmentation Model across different FOVs Figure 2 illustrates the segmentation results generated by the µCT-guided AI model applied to both large- and limited-FOV CBCT images without interpolation. The model achieved superior performance in tooth TS across both FOVs. For RCS, the limited FOV demonstrated significantly better performance in segmenting small root canals near the apex compared to the large FOV. Detailed quantitative results are presented in Table 1 . Table 1 Quantitative results of µCT-guided AI segmentation model for tooth and root canal (mean ± standard deviation) in terms of the DSC, SEN, and IOU. DSC (%) SEN (%) IOU (%) Tooth segmentation Large FOV 96.38 ± 0.80 97.35 ± 1.43 93.54 ± 1.34 Limited FOV 96.65 ± 0.68 97.09 ± 1.06 94.24 ± 1.27 Root canal segmentation Large FOV 75.99 ± 10.08 75.86 ± 11.99 66.48 ± 10.86 Limited FOV 85.25 ± 5.37 85.46 ± 7.24 76.65 ± 7.29 Comparison of AI segmentation model performances Both AI segmentation models consistently outperformed the global thresholding algorithm across all FOVs (Fig. 3 ). The µCT-guided model achieved significantly higher DSC and IoU values for TS and RCS compared to the manual-label-based model ( p < 0.05), although it showed a slight decrease in SEN ( p < 0.05) (Fig. 3 C). Variations in root canal diameter and morphology significantly influenced the evaluation metrics, with both models encountering challenges in accurately segmenting small root canals with complex anatomical variations. Specifically, the manual-label-based model tended to overestimate tooth structure boundaries in cases of narrow and irregular root canals, particularly in large-FOV images, resulting in expanded predicted contours and increased unidentifiable regions (Fig. 3 B, D). Surface deviation analysis further revealed that the µCT-guided model demonstrated greater consistency in limited-FOV images compared to the manual-label-based approach. Deviations were predominantly observed in regions with fine root canals and accessory canals (Fig. 3 B). Effect of interpolation on segmentation results The 3D visualization in Fig. 4 A demonstrates the impact of varying degrees of interpolation on CBCT images across different FOVs using the µCT-guided AI segmentation model. In the large-FOV group, increasing the voxel resolution from 200 µm to 100 µm enabled the identification of a disrupted apical main root canal that was not detectable at 200 µm. Further interpolation to 40 µm resulted in smoother boundaries but failed to detect accessory canals. Conversely, in the limited-FOV group, refining the voxel resolution from 80 µm to 40 µm enhanced the segmentation of accessory canals and isthmuses. At equivalent voxel sizes, the limited-FOV group exhibited significantly superior RSC quality compared to the large-FOV group. Quantitative analysis in Fig. 4 B reveals that TS performance metrics (DSC ≥ 0.9488, SEN ≥ 0.9533, IOU ≥ 0.9181) remained consistently high across all interpolation ranges for both large- and limited-FOV images. In contrast, the evaluation metrics of RCS in large FOV images were improved at one time of interpolation, but showed a significant downward trend with more than one time of interpolation. For limited FOV images, the RCS evaluation metrics did not change significantly within the limited interpolation range in this experiment. Due to a slight difference in the ground truth among groups with different voxel after µCT data registration, no statistical analysis was performed on the differences between segmentation results among different voxel size groups. RCS from patient CBCT images In SR, 53% (9/17) of cases achieved an "excellent" rating, while 41% (7/17) were classified as "good," with no instances falling under the category of "poor." The segmentation of root canals up to the apical foramen was successfully achieved in common root canal types, as shown in Fig. 5 A-F. Even challenging cases, such as C-shaped canals (Fig. 5 B) and accessory canals (Fig. 5 D), were accurately identified. False-positive regions occasionally appeared during the root canal prediction process in M, mainly in the coronal dentin under the enamel. The ImageJ/Fiji software was utilized along with the Find Connected Regions plugin to separate predicted voxels into individual objects, enabling the removal of false-positive objects and the extraction of root canals. As illustrated in Fig. 5 G-L, only a few extremely tiny root canals failed to be continuously segmented; these segments were difficult to discern even with the naked eye. After post-processing, the evaluation results indicated that 66.7% (8/12) of cases achieved an “excellent” rating, while the remaining 33.3% (4/12) were rated as “good”. Discussion In alignment with the findings of Lin et al.[ 30 ], µCT-guided AI CBCT segmentation is superior to manual labeling. However, it is crucial to emphasize that the resolution of µCT alone does not solely determine the accuracy of the results; the voxel size of the CBCT images used for mapping also plays a significant role. The masks derived from µCT data were artificially down-sampled following registration. While appropriate interpolation can mitigate the issue of small root canals being overlooked due to large voxels, excessive interpolation may introduce overly smooth features[ 33 ]. Therefore, future improvements should focus on leveraging DL techniques for super-resolution reconstruction of CBCT images. This study suggests that a ground truth voxel size of 80 µm³ may effectively represent the anatomical morphology of most root canals without imposing excessive demands on memory or computational resources. Furthermore, our results indicate that RCS is more sensitive to scanning resolution than TS. Consequently, limited FOV images with an 80 µm³ voxel size were selected for the following clinical RCS applications. Preliminary experiments revealed that the model trained on molar M datasets was more effective in addressing under-segmentation challenges associated with the small root canals of SR. Root canals in molars are generally more complex and narrower compared to those in SR. Future research should involve a larger sample size with diverse root canal morphologies. Moreover, there should be an increased emphasis on automating tooth instance segmentation using DL to alleviate the tedious process of manually delineating tooth boundaries in clinical CBCT images. The use of patient CBCT data for external validation of the µCT-guided CBCT segmentation model introduced heterogeneity compared to the isolated teeth used in the training data. This heterogeneity was particularly evident during the segmentation of molar root canals, where occasional false-positive regions emerged and hindered automated RCS. However, such heterogeneity was rarely observed in the anterior teeth included in this study. The discrepancy in RCS performance between M and SR may be attributed to the dense bone tissue surrounding molars in CBCT images, as well as the relatively narrower diameter of their root canals compared to anterior teeth, resulting in reduced contrast between dentin and root canals. Consequently, the limitations of global threshold segmentation methods became more pronounced. Further research is necessary to develop more advanced methods to replace the global thresholding approach currently used in constructing 3D tooth models from µCT data. Conclusions In conclusion, we developed a µCT-guided AI segmentation model for isolated teeth that enables accurate and automated detection of RCS from CBCT images. The model demonstrated superior performance over traditional global thresholding and manual label-based approaches. Our findings highlight the advantages of limited FOV CBCT in detecting small apical root canals and emphasize the importance of optimal interpolation for resolving voxel-related recognition issues. By applying this model to clinical CBCT data, we achieved efficient and precise RCS visualization, offering a promising solution to simplify root canal therapy and reduce treatment complications. Abbreviations CBCT Cone-beam computed tomography μCT Micro-computed tomography AI Artificial intelligence FOV Field of view SR Single rooted teeth M Molars 3D Three-dimensional DL Deep Learning CNN Convolutional Neural Network DSC Dice Similarity Coefficient SEN Sensitivity IOU Intersection over Union TS Tooth segmentation RCS Root canal segmentation Declarations Acknowledgements Not applicable. Author contributions X.H.G., L.T.H. and B.F. conceived the experiments; X.H.G. conducted the experiments; X.HG., L.T.H. processed the data and raw images; J.Z.M., B.L. M.Z. analyzed and interpreted the results; X.H.G., L.T.H. and Y.M.F. drafted the manuscript; J.Z.M., L.T.H. and B.F. provided funding supports; all authors edited and reviewed the manuscript. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 81771067), the Key Research and Development Project of Hubei Province of China (Grant No. 2022BCA033), the Basic and Applied Basic Research Foundation of Guangdong Province (Grant No. 2022A1515110722, and 2024A1515011750), and the Guangdong Provincial Medical Science and Technology Research Fund Project (Grant No. A2023011). Data availability Most of data generated or analyzed during this study are included in this article. The original datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate All procedures were conducted in accordance with the Helsinki Declaration and approved by the Ethics Committee of the School & Hospital of Stomatology, Wuhan University (Approval No. 2021A46; Date: March 1, 2021). The tooth samples used in this study were obtained from the Tooth and Root Canal Morphology Database at the School of Stomatology, Wuhan University, China. These samples were voluntarily donated by patients who explicited authorization for their use in scientific research. All specimens were extracted teeth removed solely due to clinical treatment needs, such as severe periodontal disease or orthodontic requirements, and the extraction procedures were performed independently of this study. Additionally, the CBCT images analyzed were originally acquired for patients' diagnostic or therapeutic purposes unrelated to this research. Informed consent was obtained from all participants prior to their inclusion in the study. 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Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2026 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 30 Dec, 2025 Reviews received at journal 27 Nov, 2025 Reviews received at journal 26 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Editor invited by journal 27 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 05 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7053178","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492098865,"identity":"e7c78f2c-7444-4415-95f4-4ac4b5aaa627","order_by":0,"name":"Xianhua Gao","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Xianhua","middleName":"","lastName":"Gao","suffix":""},{"id":492098868,"identity":"a83f1f22-c343-4254-a6eb-c15fa083ab9e","order_by":1,"name":"Jingzhi Ma","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingzhi","middleName":"","lastName":"Ma","suffix":""},{"id":492098869,"identity":"2623c727-84ea-4ad9-af25-100d26b1f993","order_by":2,"name":"Bo Li","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Li","suffix":""},{"id":492098871,"identity":"b83776c4-d378-4cfb-bc62-d2df3b28e1fb","order_by":3,"name":"Yimeng Fang","email":"","orcid":"","institution":"The Data Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yimeng","middleName":"","lastName":"Fang","suffix":""},{"id":492098875,"identity":"2066e38f-12a0-47e6-8964-0a8943bf376b","order_by":4,"name":"Lianting Hu","email":"","orcid":"","institution":"The Data Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lianting","middleName":"","lastName":"Hu","suffix":""},{"id":492098876,"identity":"852b5c60-a981-45b1-bb19-5ff4cba04b65","order_by":5,"name":"Min Zhou","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhou","suffix":""},{"id":492098877,"identity":"77418810-06eb-4fa2-8a1b-59530fef525e","order_by":6,"name":"Bing Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACA2YgUcHAIMcgAeKyEavlDAODMQlaGCBaEhuI1mLOznv4xYGaO+kbbvcYMHwoO8zAP7sBvxbLZr40iwPHnuVuuHPGgHHGucMMEncOEHDYYR4z4w9sh3O33cgxYOZtO8xgIJFAWIvBgX+H081AWv4SqcX4wcG2wwlgLYzEaLFs5jFjONh32HD/jbSCgz3n0nkkbhDQYs5/xvjDgW+H5SVnJG988KPMWo5/BgEtQMAmAWMdAGIeguqBgPkDMapGwSgYBaNgBAMA0INHTrTYJT4AAAAASUVORK5CYII=","orcid":"","institution":"Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2025-07-05 12:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7053178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7053178/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12903-026-07918-2","type":"published","date":"2026-03-10T15:59:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88094461,"identity":"82df5b90-b61d-41d8-b634-f9f30a53e431","added_by":"auto","created_at":"2025-08-01 10:48:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2418579,"visible":true,"origin":"","legend":"\u003cp\u003eThe framework of the proposed segmentation process. A, TS and RCS from CBCT images of isolated teeth; B, RCS from patient CBCT.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7053178/v1/c74e794065d1777fa8296c29.png"},{"id":88096291,"identity":"0e0cce50-6663-4842-8baf-a9c11d3c03ca","added_by":"auto","created_at":"2025-08-01 10:56:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1782085,"visible":true,"origin":"","legend":"\u003cp\u003eSegmentation outcomes of the μCT-guided AI model applied to the test set from large- and limited-FOV images. A, 3D visualizations of the predicted segmentation morphology alongside the corresponding 3D reference models derived from μCT data. B, Quantitative analysis of segmentation performance, evaluated using DSC, SEN, and IOU.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7053178/v1/ab4f9cf33f8e43fdcde45ef5.png"},{"id":88094455,"identity":"9e08a36f-acf7-4416-a37a-f31922e47693","added_by":"auto","created_at":"2025-08-01 10:48:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2168726,"visible":true,"origin":"","legend":"\u003cp\u003eThe segmentation results analysis conducted on large and limited FOV images of a mandibular anterior tooth with a complex root canal using different methods. A, the comparison between thresholding method and the 2 types AI segmentation models; B, surface deviation analysis; and C, the quantitative assessment in terms of DSC, SEN, and IOU, *\u003cem\u003ep\u003c/em\u003e＜ 0.05; D, 3D superimposition analysis and cross-sectional comparisons at 4 different slices for the 2 types of AI segmentation models against the ground truth (red: ground truth, green: manual-label-based AI segmentation, blue: μCT-guided AI segmentation).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7053178/v1/96b948f23b5d5b5be10dc06e.png"},{"id":88094463,"identity":"2b2a5208-9f5e-43b3-9108-5599eb72d9ae","added_by":"auto","created_at":"2025-08-01 10:48:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2576762,"visible":true,"origin":"","legend":"\u003cp\u003e3D visualization (A) and quantitative analysis (B) of μCT-guided AI segmentation results of the test set from large and limited FOV images with different degrees of interpolation; the reference column in A showed the corresponding 3D model constructed from μCT data.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7053178/v1/032e385f8548ac2a196df31f.png"},{"id":88094467,"identity":"92e8fa5f-2546-4c61-9817-5f1608999daa","added_by":"auto","created_at":"2025-08-01 10:48:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3375668,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted root canal displayed from different anatomical planes of the patient CBCT and its 3D model. A-F, SR; G-L, M.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7053178/v1/db1eb702c29358d6afd6f2b7.png"},{"id":104740246,"identity":"d5f4f808-9396-4950-93f0-5768774de66a","added_by":"auto","created_at":"2026-03-16 16:16:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12667099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7053178/v1/78b22e00-e41d-479c-8bac-937e996c0463.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRoot canal segmentation from cone-beam computed tomography guided by micro-computed tomography based on deep learning\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eA thorough understanding of root canal anatomy is fundamental to the success of endodontic treatments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Micro-computed tomography (µCT) is widely regarded as the gold standard for imaging and analyzing root canal systems due to its ability to provide high-resolution, high-contrast images [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These images enable the use of simple thresholding techniques to automatically segment root canals, thereby facilitating accurate three-dimensional (3D) reconstructions of internal tooth structures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the clinical application of µCT is limited by its long acquisition times, high radiation doses, and narrow field of view (FOV), restricting its use primarily to imaging small live animals or specific organs in humans and large animals.\u003c/p\u003e\u003cp\u003eCone-beam computed tomography (CBCT) has emerged as a practical alternative to µCT in dental imaging, offering multi-planar visualization with a wide FOV that can range from a few teeth to full craniofacial coverage. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Its advantages include shorter scanning times and lower radiation doses, making it suitable for clinical 3D dental radiology. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite these benefits, CBCT images often suffer from blurring and are presented as two-dimensional sequences, requiring clinicians to rely heavily on their experience to interpret the internal 3D structure of teeth. Consequently, there is a pressing need to advance automated and precise root canal segmentation (RCS) techniques from CBCT images, particularly for applications in digital dentistry such as guided endodontics [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe segmentation of CBCT images presents several challenges, including noise, limited resolution, beam hardening artifacts, and the inherent morphological variability of dental anatomy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Traditional thresholding methods, which are effective for µCT segmentation, are less reliable for CBCT data [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Level Set method, which tracks contour changes across consecutive slices, is prone to over-segmentation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Graph cut algorithms require precise location information and user interaction [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while template-based fitting methods struggle with the segmentation of multi-rooted teeth[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDeep Learning (DL), a subset of artificial intelligence (AI), has recently gained significant attention for its potential in medical imaging [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. By leveraging Convolutional Neural Networks (CNNs), DL can integrate both low- and high-level features, outperforming traditional methods in accuracy and speed [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Despite these advancements, research focusing on RCS from CBCT images using DL remains limited [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Studies have explored several DL models, such as U-Net, residual U-Net, Xception U-Net, and enhanced 3D deep neural networks, for RCS from CBCT images with varying voxel sizes [\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, accurately detecting the intricate and variable features of root canals, particularly in the apical region, remains a significant challenge. Wang et al. improved this issue by optimizing the network architecture and introducing a new evaluation metric to measure distance errors near the apical foramen. However, their study had certain limitations. Firstly, the research scope was confined to anterior teeth and premolars. Secondly, the study relied on manual annotations as ground truth [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is noteworthy that manual annotation is not only time-consuming but also prone to significant intra- and inter-observer variability. Additionally, due to the inherently low accuracy of visually identifying fine and irregular root canals, this method has intrinsic limitations in terms of precision.\u003c/p\u003e\u003cp\u003eGiven that supervised DL models depend on the anatomical correspondence between the input image and the ground truth, obtaining accurate ground truth data is critical for achieving optimal results. High-precision segmentation masks can be automatically generated from µCT data using simple thresholding methods. Lin et al. adopted an innovative approach by applying a micro-CT-guided segmentation model to segment clinical CBCT images of patients. This method automatically generates high-precision segmentation masks from µCT data using a simple thresholding technique and establishes a mapping relationship with patient CBCT images through image registration. Nevertheless, the segmentation of fine root canals remains challenging. This might stem from the constraints of the training data, which exclusively included premolars extracted for orthodontic purposes, resulting in a lack of diversity in tooth types and morphological complexity.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To enhance the representativeness of the data, it is essential to include teeth from various positions and root canal systems with differing complexities. However, acquiring such a dataset—comprising pre-teeth-extraction patient CBCT images and post-teeth-extraction micro-CT images—poses significant practical challenges.\u003c/p\u003e\u003cp\u003eThe Tooth and Root Canal Morphology Database (School of Stomatology, Wuhan University, China) comprises over 3,000 extracted teeth and their corresponding µCT images will serve as a significant data support for automated segmentation of dental and root canal systems. Therefore, this study aims to explore the feasibility of applying DL segmentation models, trained on CBCT images of extracted teeth for clinical patient CBCT images segmentation. In brief, firstly, we trained, evaluated and compared the AI segmentation models for segmenting tooth and root canal from CBCT images of isolated teeth, both with and without the guidance of µCT (µCT-guided AI segmentation vs. manual-label-based AI segmentation). The effects of FOV size and interpolation algorithm on RCS performance were studied. Subsequently, the established mapping relationship between CBCT and µCT of isolated teeth was utilized for RSC from patient CBCT images. This approach enabled rapid and accurate RSC, which was validated by endodontic specialists, demonstrating its potential feasibility for clinical application.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eSamples and data collection of isolated teeth\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTeeth samples: A total of 56 extracted teeth, comprising 28 single-rooted teeth (SR) and 28 molars (M), were selected from the Tooth and Root Canal Morphology Database. All selected teeth exhibited fully formed roots and were free from cracks, restorations, root fillings, or root resorptions.\u003c/p\u003e\u003cp\u003eµCT imaging: The teeth were initially scanned using a µCT system (µCT-50; Scanco Medical, Bassersdorf, Switzerland) with the following parameters: isotropic resolution of 30 µm, scanning vial diameter of 48 mm, voltage of 90 kVp, current of 88 mA, power of 8 W, integration time of 500 ms, and 500 projections per 180°. Scanning was performed perpendicular to the longitudinal axis of the root.\u003c/p\u003e\u003cp\u003eCBCT imaging: Subsequently, the teeth were scanned using both large- and limited- FOV CBCT systems. Large-FOV CBCT scans were performed using the NewTom 5G system (Quantitative Radiology, Verona, Italy) with a FOV of 15 cm × 12 cm, isotropic resolution of 200 µm, voltage of 110 kVp, current of 3.0 mA, and exposure time of 3.6 s. Limited-FOV CBCT scans were conducted using the 3D Accuitomo 170 system (J Morita Mfg. Corp., Kyoto, Japan) with a FOV of 4 cm × 4 cm, isotropic resolution of 80 µm, voltage of 90 kV, and current of 5 mA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTraining samples acquisition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo address the significant variation in tooth size and ensure consistent image dimensions for training, the datasets for single-rooted teeth (SR) and molars (M) were preprocessed. Large- and limited-FOV CBCT volumes were cropped to dimensions of 64 × 64 and 160 × 160 for SR, and 80 × 80 and 200 × 200 for M, respectively. The depth of the cropped volumes was adjusted according to the tooth length, ensuring complete coverage of the tooth while retaining sufficient background context for segmentation.\u003c/p\u003e\u003cp\u003eIn the control group (manual-label-based AI segmentation), two endodontists manually annotated the tooth and root canal structures. An experienced endodontist reviewed and refined these annotations using MITK software.\u003c/p\u003e\u003cp\u003eIn the experimental group (µCT-guided AI segmentation), 3D tooth models with root canal systems were generated from µCT data using a thresholding method in Mimics software (v18.0; Materialise, Leuven, Belgium). These tooth masks, containing root canals, were imported into the Medical Imaging Interaction Toolkit Workbench (MITK, version 2022.04; available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mitk.org\u003c/span\u003e\u003cspan address=\"http://mitk.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and registered to CBCT images using the rigidICP.3D.default algorithm. Following registration, µCT masks were normalized to match the resolution of the CBCT images. Training samples were created by mapping the tooth and root canal masks onto the CBCT images.\u003c/p\u003e\u003cp\u003eFor both groups, the registered 3D models derived from µCT data served as the ground truth for the test set. The SR and M samples were divided into a training set (18 teeth), a validation set (5 teeth), and a test set (5 teeth). All data were exported in NIFTI format.\u003c/p\u003e\u003cp\u003eTo evaluate the impact of interpolation on RCS using the µCT-guided AI model, cropped images with a voxel size of 200 µm were refined to 100 µm, 80 µm, 50 µm, and 40 µm. Similarly, images with an initial voxel size of 80 µm were interpolated to 50 µm and 40 µm using linear interpolation. The 3D visualization of RCS was compared and analyzed across different FOVs and interpolation levels.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTraining and testing of AI segmentation models\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe U\u003csup\u003e2\u003c/sup\u003e-Net architecture, a robust deep CNN designed for salient object segmentation, was employed as the AI segmentation model in this study[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The model was iteratively trained to optimize performance, with the framework comprising two stages: TS and RCS, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. For the training and validation sets, RCS images were extracted within tooth mask boundaries using MITK software. In the test set, teeth were segmented based on predicted tooth contours.\u003c/p\u003e\u003cp\u003eThe DL algorithms and models were implemented using PyCharm Community Edition (Version 2022.2.3 x64) and Python (Version 3.9) on a Windows 10 system equipped with NVIDIA RTX A5000 GPU (24GB memory). The PyTorch framework (version 1.7.0; available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pytorch.org\u003c/span\u003e\u003cspan address=\"https://pytorch.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized for model implementation, with optimization performed using the Adam optimizer at an initial learning rate of 10⁻³. Training was conducted for 400 epochs per group, with durations ranging from 1.3 to 50 hours depending on dataset size. The full-sized U\u003csup\u003e2\u003c/sup\u003e-Net architecture was applied applied, and data augmentation techniques, including random rotation and clipping, were employed to mitigate overfitting.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantitative and qualitative evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe following metrics were used to evaluate the voxel-matching accuracy of the segmentation models:\u003c/p\u003e\u003cp\u003eDice Similarity Coefficient (DSC):\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}DSC\\:=\\:\\frac{2\\left|{V}_{gt}\\cap\\:\\:{V}_{seg}\\right|}{\\left|{V}_{gt}\\right|+\\left|{V}_{seg}\\right|}\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eSensitivity (SEN/Recall):\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}SEN\\:=\\frac{\\left|{V}_{gt}\\cap\\:{V}_{seg}\\right|}{\\left|{V}_{gt}\\:\\right|}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eIntersection over Union (IOU):\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}IOU=\\frac{\\left|{V}_{gt}\\cap\\:{V}_{seg}\\right|}{\\left|{V}_{gt}\\cup\\:{V}_{seg}\\right|}\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{gt}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{seg}\\)\u003c/span\u003e\u003c/span\u003e represent the voxel sets of the ground truth (label data) and the model segmentation, respectively. Higher values of DSC, SEN, and IOU indicate superior performance. Visual comparisons between manual-label-based and µCT-guided AI segmentation models were conducted using MITK and GOM Inspect Pro software (GOM Software 2022, GOM GmbH, Braunschweig, Germany). Surface deviation analysis was performed for TS (-0.5 mm to + 0.5 mm) and RCS (-0.2 mm to + 0.2 mm).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRCS from patient CBCT images\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the clinical application of the established mapping relationship between isolated teeth CBCT and µCT, limited FOV CBCT image series were collected from 20 anonymous patients. Scans were acquired using a 3D Accuitomo 170 system (J Morita Mfg. Corp., Kyoto, Japan) with a FOV of 4 cm × 4 cm, operating at 90 kV and 5 mA, and a voxel size of 80 µm³. A total of 29 teeth (17 SR and 12 M) without restorations or fractures were extracted as bounding boxes. Axial image sizes were standardized to 160 × 160 for SR and 200 × 200 for M. Teeth were manually labeled from patient CBCT scans, and regions of interest (ROIs) within tooth contours were extracted for RCS. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, the extracted tooth images were input into the pre-trained µCT-guided AI segmentation model for automatic RSC. Results were qualitatively assessed by three endodontists and categorized as “excellent”, “good”, “fair”, or “poor”. Disagreements were resolved through consensus discussions.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe normality of the data was assessed using the Shapiro-Wilk test. For paired data following a normal distribution, the paired t-test was applied, whereas the Wilcoxon signed-rank test was used for nonparametric data. A significance level of \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 was adopted for all analyses. All statistical evaluations were performed using SPSS 16.0 software.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePerformance of the \u0026micro;CT-Guided AI Segmentation Model across different FOVs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the segmentation results generated by the \u0026micro;CT-guided AI model applied to both large- and limited-FOV CBCT images without interpolation. The model achieved superior performance in tooth TS across both FOVs. For RCS, the limited FOV demonstrated significantly better performance in segmenting small root canals near the apex compared to the large FOV. Detailed quantitative results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eQuantitative results of \u0026micro;CT-guided AI segmentation model for tooth and root canal (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) in terms of the DSC, SEN, and IOU.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDSC (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSEN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIOU (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTooth segmentation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarge FOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimited FOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eRoot canal segmentation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarge FOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.99\u0026thinsp;\u0026plusmn;\u0026thinsp;10.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.86\u0026thinsp;\u0026plusmn;\u0026thinsp;11.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.48\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimited FOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.65\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of AI segmentation model performances\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBoth AI segmentation models consistently outperformed the global thresholding algorithm across all FOVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The \u0026micro;CT-guided model achieved significantly higher DSC and IoU values for TS and RCS compared to the manual-label-based model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), although it showed a slight decrease in SEN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Variations in root canal diameter and morphology significantly influenced the evaluation metrics, with both models encountering challenges in accurately segmenting small root canals with complex anatomical variations. Specifically, the manual-label-based model tended to overestimate tooth structure boundaries in cases of narrow and irregular root canals, particularly in large-FOV images, resulting in expanded predicted contours and increased unidentifiable regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, D). Surface deviation analysis further revealed that the \u0026micro;CT-guided model demonstrated greater consistency in limited-FOV images compared to the manual-label-based approach. Deviations were predominantly observed in regions with fine root canals and accessory canals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffect of interpolation on segmentation results\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 3D visualization in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA demonstrates the impact of varying degrees of interpolation on CBCT images across different FOVs using the \u0026micro;CT-guided AI segmentation model. In the large-FOV group, increasing the voxel resolution from 200 \u0026micro;m to 100 \u0026micro;m enabled the identification of a disrupted apical main root canal that was not detectable at 200 \u0026micro;m. Further interpolation to 40 \u0026micro;m resulted in smoother boundaries but failed to detect accessory canals. Conversely, in the limited-FOV group, refining the voxel resolution from 80 \u0026micro;m to 40 \u0026micro;m enhanced the segmentation of accessory canals and isthmuses. At equivalent voxel sizes, the limited-FOV group exhibited significantly superior RSC quality compared to the large-FOV group.\u003c/p\u003e\u003cp\u003eQuantitative analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB reveals that TS performance metrics (DSC\u0026thinsp;\u0026ge;\u0026thinsp;0.9488, SEN\u0026thinsp;\u0026ge;\u0026thinsp;0.9533, IOU\u0026thinsp;\u0026ge;\u0026thinsp;0.9181) remained consistently high across all interpolation ranges for both large- and limited-FOV images. In contrast, the evaluation metrics of RCS in large FOV images were improved at one time of interpolation, but showed a significant downward trend with more than one time of interpolation. For limited FOV images, the RCS evaluation metrics did not change significantly within the limited interpolation range in this experiment. Due to a slight difference in the ground truth among groups with different voxel after \u0026micro;CT data registration, no statistical analysis was performed on the differences between segmentation results among different voxel size groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRCS from patient CBCT images\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn SR, 53% (9/17) of cases achieved an \"excellent\" rating, while 41% (7/17) were classified as \"good,\" with no instances falling under the category of \"poor.\" The segmentation of root canals up to the apical foramen was successfully achieved in common root canal types, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-F. Even challenging cases, such as C-shaped canals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and accessory canals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), were accurately identified. False-positive regions occasionally appeared during the root canal prediction process in M, mainly in the coronal dentin under the enamel. The ImageJ/Fiji software was utilized along with the Find Connected Regions plugin to separate predicted voxels into individual objects, enabling the removal of false-positive objects and the extraction of root canals. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-L, only a few extremely tiny root canals failed to be continuously segmented; these segments were difficult to discern even with the naked eye. After post-processing, the evaluation results indicated that 66.7% (8/12) of cases achieved an \u0026ldquo;excellent\u0026rdquo; rating, while the remaining 33.3% (4/12) were rated as \u0026ldquo;good\u0026rdquo;.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn alignment with the findings of Lin et al.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], \u0026micro;CT-guided AI CBCT segmentation is superior to manual labeling. However, it is crucial to emphasize that the resolution of \u0026micro;CT alone does not solely determine the accuracy of the results; the voxel size of the CBCT images used for mapping also plays a significant role. The masks derived from \u0026micro;CT data were artificially down-sampled following registration. While appropriate interpolation can mitigate the issue of small root canals being overlooked due to large voxels, excessive interpolation may introduce overly smooth features[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, future improvements should focus on leveraging DL techniques for super-resolution reconstruction of CBCT images. This study suggests that a ground truth voxel size of 80 \u0026micro;m\u0026sup3; may effectively represent the anatomical morphology of most root canals without imposing excessive demands on memory or computational resources. Furthermore, our results indicate that RCS is more sensitive to scanning resolution than TS. Consequently, limited FOV images with an 80 \u0026micro;m\u0026sup3; voxel size were selected for the following clinical RCS applications.\u003c/p\u003e\u003cp\u003ePreliminary experiments revealed that the model trained on molar M datasets was more effective in addressing under-segmentation challenges associated with the small root canals of SR. Root canals in molars are generally more complex and narrower compared to those in SR. Future research should involve a larger sample size with diverse root canal morphologies. Moreover, there should be an increased emphasis on automating tooth instance segmentation using DL to alleviate the tedious process of manually delineating tooth boundaries in clinical CBCT images.\u003c/p\u003e\u003cp\u003eThe use of patient CBCT data for external validation of the \u0026micro;CT-guided CBCT segmentation model introduced heterogeneity compared to the isolated teeth used in the training data. This heterogeneity was particularly evident during the segmentation of molar root canals, where occasional false-positive regions emerged and hindered automated RCS. However, such heterogeneity was rarely observed in the anterior teeth included in this study. The discrepancy in RCS performance between M and SR may be attributed to the dense bone tissue surrounding molars in CBCT images, as well as the relatively narrower diameter of their root canals compared to anterior teeth, resulting in reduced contrast between dentin and root canals. Consequently, the limitations of global threshold segmentation methods became more pronounced. Further research is necessary to develop more advanced methods to replace the global thresholding approach currently used in constructing 3D tooth models from \u0026micro;CT data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we developed a \u0026micro;CT-guided AI segmentation model for isolated teeth that enables accurate and automated detection of RCS from CBCT images. The model demonstrated superior performance over traditional global thresholding and manual label-based approaches. Our findings highlight the advantages of limited FOV CBCT in detecting small apical root canals and emphasize the importance of optimal interpolation for resolving voxel-related recognition issues. By applying this model to clinical CBCT data, we achieved efficient and precise RCS visualization, offering a promising solution to simplify root canal therapy and reduce treatment complications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eCBCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eCone-beam computed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e\u0026mu;CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eMicro-computed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eArtificial intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eFOV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eField of view\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eSingle rooted teeth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eMolars\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eThree-dimensional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eDeep Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eCNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eConvolutional Neural Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eDSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eDice Similarity Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eSEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eIOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eIntersection over Union\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eTooth segmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eRCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eRoot canal segmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.H.G., L.T.H. and B.F. conceived the experiments; X.H.G. conducted the experiments; X.HG., L.T.H. processed the data and raw images; J.Z.M., B.L. M.Z. analyzed and interpreted the results; X.H.G., L.T.H. and Y.M.F. drafted the manuscript; J.Z.M., L.T.H. and B.F. provided funding supports; all authors edited and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 81771067), the Key Research and Development Project of Hubei Province of China (Grant No. 2022BCA033), the Basic and Applied Basic Research Foundation of Guangdong Province (Grant No. 2022A1515110722, and 2024A1515011750), and the Guangdong Provincial Medical Science and Technology Research Fund Project (Grant No. A2023011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost of data generated or analyzed during this study are included in this article. The original datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were conducted in accordance with the Helsinki Declaration and approved by the Ethics Committee of the School \u0026amp; Hospital of Stomatology, Wuhan University (Approval No. 2021A46; Date: March 1, 2021). The tooth samples used in this study were obtained from the Tooth and Root Canal Morphology Database at the School of Stomatology, Wuhan University, China. These samples were voluntarily donated by patients who explicited authorization for their use in scientific research. All specimens were extracted teeth removed solely due to clinical treatment needs, such as severe periodontal disease or orthodontic requirements, and the extraction procedures were performed independently of this study. Additionally, the CBCT images analyzed were originally acquired for patients\u0026apos; diagnostic or therapeutic purposes unrelated to this research. Informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed HMA, Ibrahim N, Mohamad NS, Nambiar P, Muhammad RF, Yusoff M, Dummer PMH. Application of a new system for classifying root and canal anatomy in studies involving micro-computed tomography and cone beam computed tomography: Explanation and elaboration. 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A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography. J Endod.\u003cem\u003e \u003c/em\u003e2021; 47:1907-1916.\u003c/li\u003e\n\u003cli\u003eZhang J, Xia W, Dong J, Tang Z, Zhao Q. Root Canal Segmentation in CBCT Images by 3D U-Net with Global and Local Combination Loss. Annu Int Conf IEEE Eng Med Biol Soc.\u003cem\u003e \u003c/em\u003e2021; 2021:3097-3100.\u003c/li\u003e\n\u003cli\u003eLin X, Fu Y, Ren G, Yang X, Duan W, Chen Y, Zhang Q. Micro-Computed Tomography-Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography. J Endod.\u003cem\u003e \u003c/em\u003e2021; 47:1933-1941.\u003c/li\u003e\n\u003cli\u003eWang Y, Xia W, Yan Z, Zhao L, Bian X, Liu C, Qi Z, Zhang S, Tang Z. Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning. Med Image Anal.\u003cem\u003e \u003c/em\u003e2023; 85:102750.\u003c/li\u003e\n\u003cli\u003eQin X, Zhang Z, Huang C, Dehghan M, R. ZO, Jagers，Martin. U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection. Pattern Recognition.\u003cem\u003e \u003c/em\u003e2020; 106.\u003c/li\u003e\n\u003cli\u003eWu ZX, Chen XH, Xie SM, Shen J, Zeng Y. Super-resolution of brain MRI images based on denoising diffusion probabilistic model. Biomed Signal Proces.\u003cem\u003e \u003c/em\u003e2023; 85.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, AI segmentation mode, root canal segmentation, cone-beam computed tomography, micro-computed tomography","lastPublishedDoi":"10.21203/rs.3.rs-7053178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7053178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAccurate root canal segmentation from cone-beam computed tomography (CBCT) is essential for endodontic diagnosis and treatment planning. This study aims to explore the feasibility of using deep learning (DL) models, trained on CBCT images of extracted teeth guided by micro-computed tomography (\u0026micro;CT), for clinical CBCT image segmentation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA dataset of 56 extracted teeth with diverse root canal complexities was constructed, combining CBCT and \u0026micro;CT scans. Ground truth annotations were derived from \u0026micro;CT-based masks and registered to CBCT images. DL models based on U\u003csup\u003e2\u003c/sup\u003e-Net architecture were trained and evaluated for tooth and root canal segmentation, comparing \u0026micro;CT-guided and manual-label-based approaches. The effects of field-of-view (FOV) size and interpolation algorithms on segmentation performance were investigated. The trained models were applied to clinical CBCT images, achieving rapid and accurate root canal segmentation validated by endodontic specialists.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe \u0026micro;CT-guided AI segmentation method outperformed the manual-label-based approach. Combining a limited FOV with an interpolation algorithm demonstrated notable advantages in capturing intricate details. In segmenting root canal from patient CBCT images, 94% of single rooted teeth and 100% of molars, were rated as \u0026ldquo;excellent\u0026rdquo; or \u0026ldquo;good\u0026rdquo;.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003e Results demonstrated the potential of \u0026micro;CT-guided DL models for enhancing root canal segmentation in clinical practice, offering a promising tool for digital dentistry.\u003c/p\u003e\u003ch2\u003eClinical trial number:\u003c/h2\u003e\u003cp\u003enot applicable\u003c/p\u003e","manuscriptTitle":"Root canal segmentation from cone-beam computed tomography guided by micro-computed tomography based on deep learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-01 10:48:21","doi":"10.21203/rs.3.rs-7053178/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T10:23:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T15:29:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T05:35:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266499498654594904647872293240208566487","date":"2025-11-17T06:26:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3069336888877018695850123945197667020","date":"2025-11-14T13:33:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T09:55:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333143357912524871482048802676274075318","date":"2025-11-05T09:07:33+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-27T04:41:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T00:14:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331998136350430610970929656678018426446","date":"2025-09-17T17:25:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T13:18:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T11:22:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T11:20:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-07-05T12:51:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"562e927c-66aa-4730-a6e8-4b1d338f04e7","owner":[],"postedDate":"August 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:11:30+00:00","versionOfRecord":{"articleIdentity":"rs-7053178","link":"https://doi.org/10.1186/s12903-026-07918-2","journal":{"identity":"bmc-oral-health","isVorOnly":false,"title":"BMC Oral Health"},"publishedOn":"2026-03-10 15:59:42","publishedOnDateReadable":"March 10th, 2026"},"versionCreatedAt":"2025-08-01 10:48:21","video":"","vorDoi":"10.1186/s12903-026-07918-2","vorDoiUrl":"https://doi.org/10.1186/s12903-026-07918-2","workflowStages":[]},"version":"v1","identity":"rs-7053178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7053178","identity":"rs-7053178","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}